Machine Learning Quantum Dynamics using Classical Coarse-Graining

What is it about?

The vibrational spectra of condensed and gas-phase systems are influenced by the quantum-mechanical behavior of light nuclei. Full-dimensional simulations of approximate quantum dynamics are possible thanks to the imaginary time path-integral (PI) formulation of quantum statistical mechanics, albeit at a high computational cost which increases sharply with decreasing temperature. By leveraging advances in machine-learned coarse-graining, we develop a PI method with the reduced computational cost of a classical simulation. We also propose a simple temperature elevation scheme to significantly attenuate the artifacts of standard PI approaches as well as eliminate the unfavorable temperature scaling of the computational cost. We illustrate the approach, by calculating vibrational spectra using standard models of water molecules and bulk water, demonstrating significant computational savings and dramatically improved accuracy compared to more expensive reference approaches. Our simple, efficient, and accurate method has prospects for routine calculations of vibrational spectra for a wide range of molecular systems - with an explicit treatment of the quantum nature of nuclei.

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Photo by engin akyurt on Unsplash

Photo by engin akyurt on Unsplash

Why is it important?

Our work provides a simple, computationally efficient, and accurate approach to simulating quantum dynamics. The proposed technique improves the computational cost scaling of modeling quantum dynamics from O(1/T), a diverging computational cost at cryogenic temperatures, to a constant. Furthermore, it dramatically improves the accuracy of "classical" modeling quantum dynamics.

Perspectives